LEADER 02844nam0 22005173i 450 001 VAN0214469 005 20230606100444.397 017 70$2N$a9783030198947 100 $a20210920d2019 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $aAdvanced Materials$eProceedings of the International Conference on ?Physics and Mechanics of New Materials and Their Applications?, PHENMA 2018$fIvan A. 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A.$zParinov, Ivan A.$3VANV201133 790 1$aParinov, I.A.$zParinov, Ivan A.$3VANV201134 801 $aIT$bSOL$c20240614$gRICA 856 4 $uhttp://doi.org/10.1007/978-3-030-19894-7$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN0214469 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08CONS e-book 3828 $e08eMF3828 20210920 996 $aAdvanced materials$9802490 997 $aUNICAMPANIA LEADER 04142nam 2201285z- 450 001 9910557295003321 005 20210501 035 $a(CKB)5400000000041089 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69345 035 $a(oapen)doab69345 035 $a(EXLCZ)995400000000041089 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEmerging Sensor Technology in Agriculture 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (240 p.) 311 08$a3-03943-613-9 311 08$a3-03943-614-7 330 $aDigital agriculture is gaining traction among scientists implementing different new and emerging sensor technologies to monitor complex soil-plant-atmosphere interactions in an accurate, cost-effective and user-friendly manner. This book presents some of the latest advances in this emerging area of research. The diversity of applications in which digital agriculture can make an important difference in day-to-day farming decision making makes this discipline an important focus of research internationally. 606 $aGeography$2bicssc 606 $aResearch & information: general$2bicssc 610 $a3D crop modeling 610 $aacceleration sensor 610 $aadaptive thresholding 610 $aairflow field test 610 $aanimal detection 610 $aapple orchards 610 $aartificial intelligence 610 $aartificial neural networks 610 $abunch area 610 $abunch volume 610 $abushfires 610 $acapacitor sensor 610 $aCFD 610 $aCitrus sinensis L. Osbeck 610 $acluster morphology 610 $acocoa beans 610 $acolour thresholding 610 $acomputer vision 610 $acrop growth 610 $adeep learning 610 $adeposit mass 610 $adepth images 610 $aethylene gas detection 610 $aformulations 610 $afruit detection 610 $afruit ripeness 610 $aGaussian processes 610 $aguar 610 $aimage analysis 610 $aimage processing 610 $aimage segmentation 610 $ainfrared 610 $ainfrared thermography 610 $aionization 610 $aKinect sensor 610 $aleaf area index 610 $alogistic regression 610 $amachine learning 610 $amechanical harvesting 610 $amesh 610 $amodeling and simulation 610 $amonitoring method 610 $an/a 610 $anear-infrared spectroscopy 610 $anon-invasive sensing technologies 610 $aon-ground sensing 610 $aparameter acquisition 610 $aparameter tuning 610 $apartial least square 610 $apesticide droplets 610 $aphenomics 610 $aphenotype 610 $aphenotyping 610 $apigeon pea 610 $apoint cloud 610 $apose estimation 610 $aprecision livestock 610 $aprecision viticulture 610 $aproximal sensing 610 $aremote sensing 610 $aRGB 610 $aRGB-D 610 $asmoke taint 610 $asoybean 610 $aspectral sensor 610 $astress 610 $asupport vector machine 610 $asurface reconstruction 610 $atepary bean 610 $aTriticum aestivum 610 $aunmanned aerial vehicles 610 $avibration time 610 $aVitiCanopy app 610 $avolatile compounds 610 $awater deficit 615 7$aGeography 615 7$aResearch & information: general 700 $aFuentes$b Sigfredo$4edt$01280644 702 $aPoblete-Echeverria$b Carlos$4edt 702 $aFuentes$b Sigfredo$4oth 702 $aPoblete-Echeverria$b Carlos$4oth 906 $aBOOK 912 $a9910557295003321 996 $aEmerging Sensor Technology in Agriculture$93018451 997 $aUNINA